The problem of automatically orienting a camera at a target is well known and a number of solutions that, under specific conditions and with varying efficiency, may accomplish this task. Automatic recording of sporting and other activities has been accomplished when outdoors using systems that comprise GPS (global positioning system) antennas with good results. A product sold under the trademark SOLOSHOT®, designed, manufactured, and distributed by Soloshot, Inc., achieved considerable commercial success by providing instrumentation for automatic video recording that largely solved the problem of not having human camera operators available for filming amateur sportsmen and sportswomen for extended periods of time. Systems and instruments that rely on GPS signals are, however, subject to numerous limitations, the most important one being that GPS signals are not (or are not sufficiently) detectable indoors. None of the known indoor automatic camera orientation methods are sufficiently convenient and accurate for high quality video recording of, for example, sporting events, especially if they are operated by individuals rather than by the venue operators. It should be noted that here and elsewhere in this disclosure high quality video recording (HQVR) refers not to the quality of the camera, resolution, film or sensor, color filters, data compression methods and the like, but to the orienting of the camera in a way that the recording shows the intended subject at all times, the camera movements are smooth, and the focus and zoom of the camera are appropriate to show the subject.
Indoor automated video recording may be based on indoor or local positioning. Known indoor positioning systems include Wi-Fi based systems, grid layout systems, systems based on magnetic positioning, and systems based on dead reckoning. Such known indoor systems may use line of sight methods that are based on infrared or ultrasonic radiation emanating from a tag (sometimes called a beacon or remote unit) and detected by a controller system. (Note: some publications consider the tag to be “local” and the camera and associated hardware is denoted as “remote”. This usage is not widely accepted. To avoid confusion such terminology will be avoided in this disclosure. In other words, the tag is considered remote and the camera (or pointer) and associated hardware are considered local.) Known positioning systems also use shape recognition, face recognition, and image recognition methods for indoor positioning; all such methods will be referred to in this disclosure as computer vision (CV). Each of the aforementioned methods have their advantages but also their weaknesses.
Wi-Fi based systems rely on measuring the signal strength received by multiple access points to determine the location of a target. Such methodology is highly inaccurate (on the order of two to four meters) and is easily disrupted by changes in the environment, such as, for example, shifted furniture. Wi-Fi based systems (as some others, like grid layout systems or traditional trilateration systems) require equipment to be set up and calibrated in known locations to accurately determine the location of the target being tracked.
Grid layout systems and systems using radio frequency identification (RFID) sensors require multiple sensors in known locations. These systems do not work well or at all outside of the designated area. If any sensors within a designated grid are moved, the readings are off. This is also a problem with trilateration systems. An example of such system is described by Tariolle et al. US Patent Application US 201610091349 (to be referred to as Tariolle). In paragraph [0008] Tariolle states “Studies have been made on auto-calibration of sensor nodes for 2D tracking, and mainly for indoors, due to applicative context and GPS availability. Trilaterations are the main tools for auto-calibration sometimes known as SLAT (Simultaneous Localization and Tracking), as explained in Jorge Guevara et al., “Auto-localization algorithm for local positioning systems”, Ad Hoc Networks, Vol. 10, pp. 1090-1100, 2012.” Tariolle further states in paragraph [0024]: “The object may be tracked using a strategy similar to that used by the auto-calibration described above to infer the anchor positions, such as the Kalman filter-based or extended Kalman filter-based strategy, and the Bayesian approaches, for example.”
Others known positioning systems, such as magnetic positioning (that provide about one to two meters of accuracy), lack the accuracy to do what is necessary to orient a camera at a particular person for high quality video recording.
Other forms of indoor tracking that use dead reckoning are highly prone to drift and become much less accurate over time.
None of the known methods used for indoor positioning systems are accurate enough for high quality video recording while at the same time permitting filming at a variety of distances (not just at close range as in, for example, methods using infrared radiation). The known methods are also inconvenient, cumbersome, and require complicated and time consuming set-up and/or calibration prior to use.
Known GPS based cooperative tracking systems cannot operate indoors because the GPS signal detection is unreliable at best. Such systems also require calibration that takes about 15 minutes to complete and require users to learn a multi-step setup procedure. For many users, this appears too cumbersome and lengthy. Further, once such a system for automated recording is set up, the camera cannot be moved to another location without repeating the calibration.
A need exists for an automatic video recording system that provides a mobile camera that can be accurately aimed at a target (sometimes also called the object or the subject of the recording) without cumbersome equipment or lengthy system calibration procedures. Accurate aiming is a prerequisite to using high zoom when the target is not in close proximity to the recording apparatus. Further, to make such automatic video recording systems useful for virtually any user anywhere, both outdoors and indoors, one should avoid relying on preinstalled sensors, permanent fixtures at known locations, system calibration, and the like. The system should also remain operating when the recorded object or person is temporarily obscured, such as when the object or person is behind a structure, barrier, furniture, etc. (a problem when using known line of sight methods). Even further, tracking of the person or object should not be impeded by the recorded person's body movements that might interfere with the emitted infrared signal or even with image recognition.
One problem solved by the systems and methods of the present disclosure is how to have a camera track an object or person indoors or outdoors using a single fixture (i.e., camera plus positioning equipment) that is not at a permanent location, does not require calibration, that is not overly dependent on line of sight, and has a high degree of accuracy.